Multi-Agent Reinforcement Learning for Energy Networks: Computational Challenges, Progress and Open Problems
It addresses the problem of managing dynamic energy networks with renewable resources for energy sector stakeholders, but is incremental as a survey paper.
The survey explores how multi-agent reinforcement learning can support the decentralization and decarbonization of energy networks by addressing computational challenges, reviewing recent progress, and highlighting open problems, without presenting new experimental results.
The rapidly changing architecture and functionality of electrical networks and the increasing penetration of renewable and distributed energy resources have resulted in various technological and managerial challenges. These have rendered traditional centralized energy-market paradigms insufficient due to their inability to support the dynamic and evolving nature of the network. This survey explores how multi-agent reinforcement learning (MARL) can support the decentralization and decarbonization of energy networks and mitigate the associated challenges. This is achieved by specifying key computational challenges in managing energy networks, reviewing recent research progress on addressing them, and highlighting open challenges that may be addressed using MARL.